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| Titel |
A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3 |
| VerfasserIn |
R. M. Maxwell, L. E. Condon, S. J. Kollet |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 3 ; Nr. 8, no. 3 (2015-03-31), S.923-937 |
| Datensatznummer |
250116196
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| Publikation (Nr.) |
 copernicus.org/gmd-8-923-2015.pdf |
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| Zusammenfassung |
| Interactions between surface and groundwater systems are well-established
theoretically and observationally. While numerical models that solve both
surface and subsurface flow equations in a single framework (matrix) are
increasingly being applied, computational limitations have restricted their
use to local and regional studies. Regional or watershed-scale simulations
have been effective tools for understanding hydrologic processes; however,
there are still many questions, such as the adaptation of water resources to
anthropogenic stressors and climate variability, that can only be answered
across large spatial extents at high resolution. In response to this grand
challenge in hydrology, we present the results of a parallel, integrated
hydrologic model simulating surface and subsurface flow at high spatial
resolution (1 km) over much of continental North America (~ 6.3 M km2). These simulations provide integrated predictions of
hydrologic states and fluxes, namely, water table depth and streamflow, at
very large scale and high resolution. The physics-based modeling approach
used here requires limited parameterizations and relies only on more
fundamental inputs such as topography, hydrogeologic properties and climate
forcing. Results are compared to observations and provide mechanistic
insight into hydrologic process interaction. This study demonstrates both
the feasibility of continental-scale integrated models and their utility for
improving our understanding of large-scale hydrologic systems; the
combination of high resolution and large spatial extent facilitates analysis
of scaling relationships using model outputs. |
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